Systems and Methods For Guidewire Tracking Using Phase Congruency

ABSTRACT

A method of tracking a guidewire in video imagery includes: obtaining a first video image including pixels associated with features of a guidewire; selecting a set of parameters to define an open curve on the first video image; determining a feature map of the first video image using phase congruency; and updating the parameters of the open curve using the feature map to align the open curve to the pixels associated with the features of the guidewire.

CROSS-REFERENCE TO RELATED PATENT APPLICATION

This application claims the benefit of U.S. Provisional Application Ser.No. 60/742,993 (Attorney Docket No. 2005P22362US01), filed Dec. 7, 2005and entitled “A Variational Approach to Guidewire Tracking Using PhaseCongruency,” the content of which is herein incorporated by reference inits entirety.

BACKGROUND OF THE INVENTION

1. Technical Field

The present disclosure relates to image processing and, moreparticularly, to guidewire tracking using phase congruency.

2. Discussion of Related Art

Endovascular interventions are becoming increasingly more common in thetreatment of arterial disease like atherosclerosis. In such procedures,a guidewire is placed in the femoral artery in the groin and advancedtowards the heart. Central to this process is accurate placement of theguidewire with respect to the vascular anatomy, which can be imagedusing X-ray fluoroscopy. However, placement is often difficult due tothe complexity of the vasculature, patient motion and, in the case ofX-ray video, the low signal-to-noise ratio of the video that is a resultof trying to minimize the patient radiation exposure.

Tracking a guidewire to a targeted site has many applications, such asfor example, endovascular procedures. During an endovascular procedure,it would be beneficial to adaptively enhance the image around theguidewire location to reduce the noise and increase the guidewireconspicuity. Such enhancements would require an accurate determinationof the image pixels that represent the guidewire.

SUMMARY OF THE INVENTION

According to an exemplary embodiment of the present invention, a methodis provided for tracking a guidewire in video imagery. The methodincludes: obtaining a first video image including pixels associated withfeatures of a guidewire; selecting a set of parameters to define an opencurve on the first video image; determining a feature map of the firstvideo image using phase congruency; and updating the parameters of theopen curve using the feature map to align the open curve to the pixelsassociated with the features of the guidewire.

In a method for tracking a guidewire in video imagery, according to anexemplary embodiment of the present invention, when it is determinedthat convergence has occurred, and when there is a second video image toprocess, feature maps may be cross-correlated to find the displacementof the open curve. When there is a second video image to process, theopen curve position in the second video image may be set using thedisplacement.

According to an exemplary embodiment of the present invention, a systemfor tracking a guidewire in video imagery comprises: a memory device forstoring a program; a processor in communication with the memory device,the processor operative with the program to: obtain a first video imageincluding pixels associated with features of a guidewire; select a setof parameters to define an open curve on the first video image;determine a feature map of the first video image using phase congruency;and update the parameters of the open curve using the feature map toalign the open curve to the pixels associated with the features of theguidewire.

When it is determined that convergence has occurred, and when there is asecond video image to process, the processor may be further operativewith the program to cross-correlate feature maps to find thedisplacement of the open curve. When there is a second video image toprocess, the processor may be further operative with the program to setan open curve position in the second video image using the displacement.

According to an exemplary embodiment of the present invention, a methodis provided for tracking a guidewire in video imagery. The methodincludes: obtaining a first video image including features of aguidewire; selecting a set of control points for defining a spline onthe first video image; determining a feature map of the first videoimage using phase congruency; determining a gradient of the feature map,determining an arc length of the spline; determining curvature, blendingcoefficients, tangents and normals at a plurality of points on thespline; determining a system of linear equations using the curvature,blending coefficients, tangents and normals; inverting the system oflinear equations to obtain the differential motion of the controlpoints; and evolving the control points.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will become more apparent to those of ordinaryskill in the art when descriptions of exemplary embodiments thereof areread with reference to the accompanying drawings.

FIG. 1 is a flowchart showing a method of tracking a guidewire in videoimagery, according to an exemplary embodiment of the present invention.

FIG. 2 illustrates a computer system for implementing a method oftracking a guidewire in video imagery, according to an exemplaryembodiment of the present invention.

FIG. 3 is a flowchart showing a method of tracking a guidewire in videoimagery, according to an exemplary embodiment of the present invention.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

Hereinafter, exemplary embodiments of the present invention will bedescribed in detail with reference to the accompanying drawings.

A guidewire may be modeled as an open contour, such as a spline. In anexemplary embodiment of the present invention, a method of tracking aguidewire in video imagery uses phase congruency as an image-basedfeature to which the spline is fitted. For example, the video may becardiac and/or vascular X-ray video. The X-ray video may have poor, orlow, contrast.

A method of tracking a guidewire in video imagery, according to anexemplary embodiment of the present invention, evolves control points ofthe spline subject to forces that force the spline to align to theguidewire pixels detected using phase congruency, remain smooth, andpreserve an a priori length. A method of tracking a guidewire in videoimagery may use variational calculus and deform the spline subject toimage-based and intrinsic forces that require the spline to fit theimage data, be smooth, and have an a priori length.

A method of tracking a guidewire in video imagery may use a framework inwhich partial differential equations are derived that perform gradientdescent on an energy functional. Other numerical schemes that may beused to optimize the control point locations include coordinate descent,conjugate gradient descent, Newton's method, and the Levenberg-Marquardtalgorithm.

Hereinafter, methods for deriving the evolution of the spline usingvariational calculus, according to exemplary embodiments of the presentinvention, will be described with reference to Equations 1-18.

The guidewire can be represented as an open curve, as given by Equation1, in the image plane.C=[x(s),y(s)]^(T)  (1)According to exemplary embodiments of the present invention, the curveis modeled as a spline using control points P_(i), i=1 . . . N, where Nis the number of control points.

The energy E of the curve may be defined in the following terms:E(C)=data+smoothness+length constraint. The data term will require thatthe spline pass through the features detected from the image. Thesmoothness term will require that the curve to be smooth. The lengthconstraint term penalizes the curve's length from deviating from a knownlength. These energies can be scaled by constant weight factors, w₁, w₂,and w₃, respectively. The energy of the curve can be expressed asEquation 2. $\begin{matrix}{{{E(C)} = {{w_{1}{\int_{C}^{\quad}{F{\mathbb{d}s}}}} + {w_{2}{\int_{C}^{\quad}{\mathbb{d}s}}} + {w_{3}\left( {{\int_{C}^{\quad}{\mathbb{d}s}} - L_{0}} \right)}^{2}}},} & (2)\end{matrix}$where F(x,y) is a feature map computed from the image, and L₀ is the apriori length of the curve.

X-ray videos typically have reduced contrast as a result of minimizingpatient exposure to radiation. Traditional edge detection methods, suchas those that rely on the gradient, may produce a weak response undersuch conditions. An edge detection method based on phase congruencyaccording to an exemplary embodiment of the present invention mayproduce a stronger response even when the image contrast is low. In anexemplary embodiment of the present invention, the feature map F(x,y) isa function of the phase congruency edge detection image.

The partial derivative of E(C) may be taken with respect to anindependent parameter t to obtain a curve evolution that minimizes theenergy. Starting with the second term in Equation 2, the derivation canbe given by: $\begin{matrix}\begin{matrix}{{\frac{\partial}{\partial t}w_{2}{\int_{C}^{\quad}{\mathbb{d}s}}} = {w_{2}\frac{\partial}{\partial t}{\int_{0}^{1}{{C_{p}}{\mathbb{d}p}}}}} \\{{= {{w_{2}\int_{0}^{1}} < C_{p}}},{C_{p} >^{\frac{1}{2}}{\mathbb{d}p}}} \\{= {w_{2}{\int_{0}^{1}{\frac{{< C_{pt}},{C_{p} >}}{C_{p}}{\mathbb{d}p}}}}} \\{{= {{w_{2}\int_{0}^{1}} < C_{pt}}},{T > {\mathbb{d}p}}} \\{{= {{w_{2}\int_{0}^{1}} < C_{t}}},{{T >}|_{p = 0}^{1}{{{- w_{2}}\int_{0}^{L}} < C_{t}}},{{\kappa\quad N} > {\mathbb{d}s}}}\end{matrix} & (3)\end{matrix}$where <,>denotes an inner product operator, N is the curve normal, T isthe curve tangent, and κ is the curvature.

A curve evolution that minimizes the energy of the second term,according to an exemplary embodiment of the present invention, is givenby Equation 4. $\begin{matrix}{\frac{\partial C}{\partial t} = {{w_{2}\kappa\quad N} + {w_{2}{\delta(p)}T} - {w_{2}{\delta\left( {p - 1} \right)}T}}} & (4)\end{matrix}$

For the first term in Equation 2, the derivation can be given by:$\begin{matrix}\begin{matrix}{{\frac{\partial}{\partial t}w_{1}{\int_{C}^{\quad}{F{\mathbb{d}s}}}} = {w_{1}{\int_{0}^{1}{\frac{\partial}{\partial t}F{C_{p}}{\mathbb{d}p}}}}} \\{= {w_{1}{\int_{0}^{1}{\left( {{{F\frac{\partial}{\partial t}} < C_{p}},{C_{p} >^{\frac{1}{2}}{{+ \frac{\partial F}{\partial t}}{C_{p}}}}} \right){\mathbb{d}p}}}}} \\{= {w_{1}{\int_{0}^{1}{\left( {{F < C_{pt}},{T > {+ {< {\nabla F}}}},{C_{t} > {C_{p}}}} \right){\mathbb{d}p}}}}} \\{{= {{w_{1}{\int_{0}^{1}F}} < C_{p\quad t}}},{T > {{\mathbb{d}p} + {w_{1}\int_{0}^{L}}} < {\nabla F}},{C_{t} > {\mathbb{d}s}}} \\{= \begin{matrix}{{{w_{1}{\int_{0}^{1}F}} < C_{t}},{{T >}|_{p = 0}^{1}{{- {w_{1}\int_{0}^{L}F}} < C_{t}}},{{\kappa\quad N} > {{\mathbb{d}s} +}}} \\{{{w_{1}\int_{0}^{L}} < {\nabla F}},{C_{t} > {\mathbb{d}s}}}\end{matrix}}\end{matrix} & (5)\end{matrix}$

A curve evolution that minimizes the energy for the first term ofEquation 2, according to an exemplary embodiment of the presentinvention, is given by Equation 6. $\begin{matrix}{\frac{\partial C}{\partial t} = {{w_{1}F\quad\kappa\quad N} - {w_{1}{\nabla F}} + {w_{1}{\delta(p)}{FT}} - {w_{1}{\delta\left( {p - 1} \right)}{FT}}}} & (6)\end{matrix}$

For the last term in Equation 2, the derivation can be given by$\begin{matrix}{{\frac{\partial}{\partial t}{w_{3}\left( {{\int_{C}^{\quad}{\mathbb{d}s}} - L_{0}} \right)}^{2}} = {2{w_{3}\left( {{\int_{C}^{\quad}{\mathbb{d}s}} - L_{0}} \right)}\frac{\partial}{\partial t}{\int_{C}^{\quad}{\mathbb{d}s}}}} & (7)\end{matrix}$Using the result from Equations 3 and 4, this gives the curve evolutionthat minimizes the energy for the last term in Equation 2 as$\begin{matrix}{\frac{\partial C}{\partial t} = {2{w_{3}\left( {{\int_{C}^{\quad}{\mathbb{d}s}} - L_{0}} \right)}\left( {{\kappa\quad N} + {{\delta(p)}T} - {{\delta\left( {p - 1} \right)}T}} \right)}} & (8)\end{matrix}$

Combining Equations 4, 6, and 8 yields a partial differential equationthat minimizes Equation 2. This gives the curve evolution$\begin{matrix}{\frac{\partial C}{\partial t} = {{{- w_{1}}{\nabla F}} + {{\kappa\left( {w_{2} + {w_{1}F} + {2{w_{3}\left( {{\int_{C}^{\quad}{\mathbb{d}s}} - L_{0}} \right)}}} \right)}N} + \quad{\begin{bmatrix}{{{\delta(p)}\left( {w_{2} + {w_{1}F} + {2{w_{3}\left( {{\int_{C}^{\quad}{\mathbb{d}s}} - L_{0}} \right)}}} \right)} -} \\{{\delta\left( {p - 1} \right)}\left( {{- w_{2}} - {w_{1}F} - {2{w_{3}\left( {{\int_{C}^{\quad}{\mathbb{d}s}} - L_{0}} \right)}}} \right)}\end{bmatrix}T}}} & (9)\end{matrix}$Equation 9, which is herein referred to as the curve update equation, isindependent of the representation of the open curve.

According to an exemplary embodiment of the present invention, the curvegeometry is modeled using a spline that does not pass through thecontrol points. For example, the curve may be represented using auniform rational B-spline. Using a uniform rational B-spline, the curveis represented by M segments that interpolate the N=M+3 control points.In this example, the j th segment is a weighted combination of 4 controlpoints, as given by Equation 12. $\begin{matrix}{{{C_{j}(s)} = {\sum\limits_{j}^{j + 3}{{B_{j}(s)}P_{j}}}},} & (12)\end{matrix}$where j=1 . . . M, s=∈[0,1]. The parametrization variable s is used tosample B_(j), which are third order blending functions, and which can begiven as Equation 13. $\begin{matrix}{\frac{1}{6}\left\lbrack {{{- s^{3}} + {3s^{2}} - {3s} + 1},{{3s^{3}} - {6s^{2}} + 4},{{{- 3}s^{3}} + {3s^{2}} + {3s} + 1},s^{3}} \right\rbrack}^{T} & (13)\end{matrix}$

Inversion of Equation 12 expresses P_(j) as a function of C_(j)(s), andsubsequent differentiation yields a differential relationship describinghow the motion of the curve segment affects the control points. Theinversion requires that the curve is sampled N times, where N is thenumber of control points, to obtain a determined system of equations.The number of samples may be much larger than N, resulting in anover-determined system of equations.

For example, in the case when there are M=2 segments, corresponding toN=5 control points, and each of the segments is sampled L=2 times, thesystem of equations can be written as $\begin{matrix}{\begin{bmatrix}{C_{1}\left( s_{1} \right)} \\{C_{1}\left( s_{2} \right)} \\{C_{1}\left( s_{3} \right)} \\{C_{1}\left( s_{4} \right)} \\{C_{1}\left( s_{1} \right)} \\{C_{1}\left( s_{2} \right)} \\{C_{1}\left( s_{3} \right)} \\{C_{1}\left( s_{4} \right)}\end{bmatrix} = {\begin{bmatrix}{a\left( s_{1} \right)} & {b\left( s_{1} \right)} & {c\left( s_{1} \right)} & {d\left( s_{1} \right)} & 0 \\{a\left( s_{2} \right)} & {b\left( s_{2} \right)} & {c\left( s_{2} \right)} & {d\left( s_{2} \right)} & 0 \\{a\left( s_{3} \right)} & {b\left( s_{3} \right)} & {c\left( s_{3} \right)} & {d\left( s_{3} \right)} & 0 \\{a\left( s_{4} \right)} & {b\left( s_{4} \right)} & {c\left( s_{4} \right)} & {d\left( s_{4} \right)} & 0 \\0 & {a\left( s_{1} \right)} & {b\left( s_{1} \right)} & {c\left( s_{1} \right)} & {d\left( s_{1} \right)} \\0 & {a\left( s_{2} \right)} & {b\left( s_{2} \right)} & {c\left( s_{2} \right)} & {d\left( s_{2} \right)} \\0 & {a\left( s_{3} \right)} & {b\left( s_{3} \right)} & {c\left( s_{3} \right)} & {d\left( s_{3} \right)} \\0 & {a\left( s_{4} \right)} & {b\left( s_{4} \right)} & {c\left( s_{4} \right)} & {d\left( s_{4} \right)}\end{bmatrix}\begin{bmatrix}P_{1} \\P_{2} \\P_{3} \\P_{4} \\P_{5}\end{bmatrix}}} & (14)\end{matrix}$where a=−s³+3s²−3s+1, b=3s³−6s²+4, c=3s³+3s²+3s+1, and d=s³ are theelements in the blending function. More generally, this system ofequations takes the form $\begin{matrix}{\begin{bmatrix}{C_{1}\left( s_{1} \right)} \\{C_{1}\left( s_{2} \right)} \\\vdots \\{C_{1}\left( s_{L} \right)} \\{C_{2}\left( s_{1} \right)} \\{C_{2}\left( s_{2} \right)} \\\vdots \\{C_{2}\left( s_{L} \right)} \\\vdots \\{C_{M}\left( s_{1} \right)} \\{C_{M}\left( s_{2} \right)} \\\vdots \\{C_{M}\left( s_{L} \right)}\end{bmatrix} = {\begin{bmatrix}{a\left( s_{1} \right)} & {b\left( s_{1} \right)} & {c\left( s_{1} \right)} & {d\left( s_{1} \right)} & 0 & \ldots & 0 \\{a\left( s_{2} \right)} & {b\left( s_{2} \right)} & {c\left( s_{2} \right)} & {d\left( s_{2} \right)} & 0 & \ldots & 0 \\\vdots & \quad & \quad & \quad & 0 & \ldots & 0 \\{a\left( s_{L} \right)} & {b\left( s_{L} \right)} & {c\left( s_{L} \right)} & {d\left( s_{L} \right)} & 0 & \ldots & 0 \\0 & {a\left( s_{1} \right)} & {b\left( s_{1} \right)} & {c\left( s_{1} \right)} & {d\left( s_{1} \right)} & 0 & \ldots \\0 & {a\left( s_{2} \right)} & {b\left( s_{2} \right)} & {c\left( s_{2} \right)} & {d\left( s_{2} \right)} & 0 & \ldots \\0 & \vdots & \quad & \quad & \quad & 0 & \ldots \\0 & {a\left( s_{L} \right)} & {b\left( s_{L} \right)} & {c\left( s_{L} \right)} & {d\left( s_{L} \right)} & 0 & \ldots \\\vdots & \vdots & \vdots & \vdots & \vdots & \vdots & \vdots \\0 & \ldots & 0 & {a\left( s_{1} \right)} & {b\left( s_{1} \right)} & {c\left( s_{1} \right)} & {d\left( s_{1} \right)} \\0 & \ldots & 0 & {a\left( s_{2} \right)} & {b\left( s_{2} \right)} & {c\left( s_{2} \right)} & {d\left( s_{2} \right)} \\0 & \ldots & 0 & \vdots & \quad & \quad & \quad \\0 & \ldots & 0 & {a\left( s_{L} \right)} & {b\left( s_{L} \right)} & {c\left( s_{L} \right)} & {d\left( s_{L} \right)}\end{bmatrix}\begin{bmatrix}P_{1} \\P_{2} \\\vdots \\P_{N}\end{bmatrix}}} & (15)\end{matrix}$or equivalently, C=BP, where C is a ML×2 matrix, B is a ML×N matrix, andP is an N×2 matrix.

In an exemplary embodiment of the present invention, the curve can besampled arbitrarily as long as there are at least N samples of thecurve. Using the pseudo-inverse of B, this general system of equationscan be inverted as shown in Equation 16.P=(B ^(T) B)⁻¹ B ^(T) C  (16)Differentiating Equation 16 results in the differential relationshipbetween the movement of the control points relative to the movement ofthe curve, $\begin{matrix}{\frac{\partial P}{\partial t} = {\left( {B^{T}B} \right)^{- 1}B^{T}\frac{\partial C}{\partial t}}} & (17)\end{matrix}$In an exemplary embodiment of the present invention, the evolution ofthe control points is derived by plugging Equation 9 into Equation 17,which yields Equation 18. $\begin{matrix}{\frac{\partial P}{\partial t} = {\left( {B^{T}B} \right)^{- 1}B^{T}\begin{Bmatrix}{{{- w_{1}}B^{T}{\nabla F}} + {{k\left( {w_{2} + {w_{1}F} + {2{w_{s}\left( {{\int_{C}{\mathbb{d}s}} - L_{0}} \right)}}} \right)}N} +} \\{\begin{bmatrix}{{{\delta(p)}\left( {w_{2} + {w_{1}F} + {2{w_{3}\left( {{\int_{C}{\mathbb{d}s}} - L_{0}} \right)}}} \right)} +} \\{\left( {p - 1} \right)\left( {{- w_{2}} - {w_{1}F} - {2{w_{3}\left( {{\int_{C}{\mathbb{d}s}} - L_{0}} \right)}}} \right)}\end{bmatrix}T}\end{Bmatrix}}} & (18)\end{matrix}$

Exemplary embodiments of the present invention may be embodied using asubset of the terms in Equation 18. For example, in an exemplaryembodiment of the present invention, all terms in Equation 18 are usedexcept the tangential ones.

FIG. 1 is a flowchart showing a method of tracking a guidewire in videoimagery, according to an exemplary embodiment of the present invention.The video imagery may be X-ray video imagery. Referring to FIG. 1, instep 110, obtain a first video image including pixels associated withfeatures of a guidewire. For example, images may be obtained from anX-ray fluoroscopy exam, where a patient is positioned in the X-raymachine and a video is taken while the doctor inserts the catheterthrough the vasculature. It is to be understood that the first videoimage may be obtained using various imaging modalities, such as forexample, conventional photography, X-ray fluoroscopy, computedtomography (CT), ultrasound, magnetic resonance (MR), positron emissiontomography (PET) and/or single photon emission computed tomography(SPECT). The first video image may be obtained using an input device,such as for example, a scanner. The first video image may be obtainedfrom a computer readable medium. Each pixel in the first video imagecorresponds to a small volume element.

In step 120, select a set of parameters to define an open curve on thefirst video image. For example, the open curve may be represented by auniform rational B-spline. Exemplary embodiments of the presentinvention may be embodied using various splines, polyline, fourierdescriptor curves, or implicit curves to model the curve.

The parameters defining the open curve may be control points that areinterpolated by the uniform rational B-spline. The parameters definingthe open curve may be selected manually, for example, based on proximityto the guidewire. A user interface including, for example, a display, akeyboard and/or a pointing device, may be employed by the user to selectthe parameters defining the open curve. The parameters defining the opencurve may be selected automatically.

In step 130, determine a feature map of the first video image usingphase congruency. In general, finding where phase congruency is amaximum is roughly equivalent to finding where the weighted variance oflocal phase angles, relative to the weighted average local phase, is aminimum. Points of maximum phase congruency can be calculated bysearching for peaks in the local energy function. That is, when thelocal energy function is directly proportional to the phase congruencyfunction, peaks in the local energy will correspond to peaks in phasecongruency. The use of phase congruency for feature detection mayprovide invariance to variations in image illumination and/or contrast.

In an exemplary embodiment of the present invention, determining afeature map of the first video image using phase congruency includesmulti-scale image analysis. Multi-scale analysis considers features atmultiple scales (sizes) in an image. For example, multi-scale imageanalysis may be performed using filter kernels that act on differentspatial frequencies of the image.

In step 140, update the parameters of the open curve using the featuremap to align the open curve to the pixels associated with the featuresof the guidewire. In an exemplary embodiment of the present invention,the update step 140 is performed by solving Equation 18, which is asystem of partial differential equations. Equation 18 includes thefeature map F, to which the open curve deforms so that it aligns withthe guidewire detected in the feature map.

When aligning the open curve to the pixels associated with the featuresof the guidewire, according to an exemplary embodiment of the presentinvention, a predetermined length of the open curve is preserved. Thispredetermined length may be determined by the length of open curvedefined by the initial set of control points. The predetermined lengthof the open curve may be determined by the length of the guidewire.

Updating the parameters defining the open curve may include energyminimization. In an exemplary embodiment of the present invention,updating the parameters defining the open curve includes using agradient descent process that minimizes an energy functional defined onthe spline. Tangential forces may be used to update the parametersdefining the open curve. A pseudo-inverse may be used to update theparameters defining the open curve.

A method of tracking a guidewire in video imagery according to anexemplary embodiment of the present invention described in connectionwith FIG. 1 may include determining whether convergence has occurred. Asused herein, the term “convergence” refers to the point at which a localminimum of an energy has been reached. For example, convergence refersto the point at which the energy E(C) in Equation 2 reaches a localminimum. Upon convergence, according to an exemplary embodiment of thepresent invention, the open curve has reached its optimal position. Uponconvergence, Equation 18 is solved.

When convergence has occurred, and when there is another video frame toprocess, a cross-correlation of feature maps may be determined to finddisplacement of the open curve. For example, this may be achieved byforming a bounding box around the open curve on the current frame. Thepixels of the current frame in the bounding box form a sub-image that iscross-correlated with the next video frame. It is to be understood thatthe next video frame may be the second video image, third video image,fourth video image, or other video image. In an exemplary embodiment ofthe present invention, the peak of the cross-correlation may give thedisplacement of the open curve on the next video frame.

A method of tracking a guidewire in video imagery, according to anexemplary embodiment of the present invention, includes: obtaining asecond video image including pixels associated with features of theguidewire; and setting an open curve position in the second video image.In an exemplary embodiment of the present invention, the open curvealigned on the first video image is positioned on the second videoimage. For example, as described above, the peak of thecross-correlation may give the displacement of the open curve on thesecond video image. After the open curve is positioned on the secondvideo image, the step 130 of determining a feature map and the step 140of updating the parameters of the open curve may be performed. Thissequence of steps may be repeated a multiple number of times or untilall of the video frames are processed.

It is to be understood that exemplary embodiments of the presentinvention may be implemented in various forms of hardware, software,firmware, special purpose processors, or a combination thereof. Forexample, exemplary embodiments of the present invention may beimplemented in software as an application program tangibly embodied on aprogram storage device. The application program may be uploaded to, andexecuted by, a machine comprising any suitable architecture.

Referring to FIG. 2, according to an exemplary embodiment of the presentdisclosure, a computer system 201 for implementing a method of trackinga guidewire in video imagery can comprise, inter alia, a centralprocessing unit (CPU) 209, a memory 203 and an input/output (I/O)interface 204. The computer system 201 may include a graphics processingunit (GPU) 202. The computer system 201 is generally coupled through theI/O interface 204 to a display 205 and various input devices 206 such asa mouse and keyboard. The support circuits can include circuits such ascache, power supplies, clock circuits, and a communications bus. Thememory 203 can include random access memory (RAM), read only memory(ROM), disk drive, tape drive, etc., or a combination thereof. Anexemplary embodiment of the present invention can be implemented as aroutine 207 that is stored in memory 203 and executed by the CPU 209 toprocess the signal from the signal source 208. As such, the computersystem 201 is a general purpose computer system that becomes a specificpurpose computer system when executing the routine 207 of the presentinvention.

The computer platform 201 also includes an operating system and microinstruction code. The various processes and functions described hereinmay either be part of the micro instruction code or part of theapplication program (or a combination thereof) which is executed via theoperating system. In addition, various other peripheral devices may beconnected to the computer platform such as an additional data storagedevice and a printing device.

In an exemplary embodiment of the present invention, a system fortracking a guidewire in video imagery comprises a memory device 203 forstoring a program, and a processor 209 in communication with the memorydevice 203. The processor 209 is operative with the program to: obtain afirst video image including pixels associated with features of aguidewire; select a set of parameters to define an open curve on thefirst video image; determine a feature map of the first video imageusing phase congruency; and update the parameters of the open curveusing the feature map to align the open curve to the pixels associatedwith the features of the guidewire.

It is to be further understood that, because some of the constituentsystem components and method steps depicted in the accompanying figuresmay be implemented in software, the actual connections between thesystem components (or the process steps) may differ depending upon themanner in which the present invention is programmed. Given the teachingsof exemplary embodiments of the present invention provided herein, oneof ordinary skill in the related art will be able to contemplate theseand similar implementations or configurations of the present invention.

FIG. 3 is a flowchart showing a method of tracking a guidewire in videoimagery, according to an exemplary embodiment of the present invention.

The video imagery may be X-ray video imagery. Referring to FIG. 3, instep 305, obtain a first video image including pixels associated withfeatures of a guidewire.

In step 310, select a set of control points for defining a spline on thefirst video image. For example, the spline may be a uniform rationalB-spline. Exemplary embodiments of the present invention may be embodiedusing various splines, polyline, fourier descriptor curves, or implicitcurves to model the curve. The set of control points may be selectedmanually, for example, based on proximity to the guidewire. The set ofcontrol points may be selected automatically.

In step 320, determine a feature map of the first video image usingphase congruency. Determining a feature map of the first video imageusing phase congruency may include multi-scale image analysis.

In step 325, determine a gradient of the feature map. When applied to adigital image, this can be accomplished using discrete differenceoperators.

In step 330, determine an arc length of the spline. This can be computedby summing the length of each segment in a sampled version of thespline, or by integrating the magnitude of the first derivative of thespline.

Determine curvature, blending coefficients, tangents and normals at aplurality of points on the spline, in step 340. The curvature, tangents,and normals can be computed from the first and second derivatives of thespline. In an exemplary embodiment of the present invention, theblending coefficients are determined from Equation 13.

In step 350, determine a system of linear equations using the curvature,blending coefficients, tangents and normals. In step 350, invert thesystem of linear equations to obtain the differential motion of thecontrol points. For example, this can be represented by Equation 17.

In step 360, evolve the control points until convergence. In anexemplary embodiment of the present invention, evolving the controlpoints until convergence provides a solution to Equation 17.

In step 375, determine whether the spline has converged. For example,convergence refers to the point at which the energy E(C) in Equation 2reaches a local minimum. When convergence has occurred, and when thereis a next video frame to process, a cross-correlation of feature mapsmay be determined to find displacement of the spline. For example, thismay be achieved by forming a bounding box around the spline on thecurrent frame. The pixels of the current frame in the bounding box forma sub-image that is cross-correlated with the next frame.

In an exemplary embodiment of the present invention, the peak of thecross-correlation gives the displacement of the spline on the next videoframe. It is to be understood that the next video frame may be thesecond video image, third video image, fourth video image, or othervideo image. In the case when convergence has not occurred, return tostep 330, as shown in FIG. 3.

When convergence has occurred, determine whether there are additionalframes to process, in step 385. In the case when there are additionalframes to process, such as for example, a second video image, return tostep 320, and determine a feature map of the second video image usingphase congruency. Repeat steps 325 through 360 and, in step 375,determine whether convergence has occurred on the second video image. Inthe case when convergence has occurred, determine whether there areadditional frames to process, in step 385.

Although exemplary embodiments of the present invention have beendescribed in detail with reference to the accompanying drawings for thepurpose of illustration, it is to be understood that the inventiveprocesses and apparatus are not to be construed as limited thereby. Itwill be readily apparent to one of ordinary skill in the art thatvarious modifications to the foregoing exemplary embodiments can be madewithout departing from the scope of the invention as defined by theappended claims, with equivalents of the claims to be included therein.

1. A method of tracking a guidewire in video imagery, comprising:obtaining a first video image including pixels associated with featuresof a guidewire; selecting a set of parameters to define an open curve onthe first video image; determining a feature map of the first videoimage using phase congruency; and updating the parameters of the opencurve using the feature map to align the open curve to the pixelsassociated with the features of the guidewire.
 2. The method of claim 1,wherein the open curve is represented by a uniform rational B-spline. 3.The method of claim 2, wherein the parameters defining the open curveare control points that are interpolated by the uniform rationalB-spline.
 4. The method of claim 1, wherein determining a feature map ofthe first video image using phase congruency comprises multi-scale imageanalysis.
 5. The method of claim 1, wherein when aligning the open curveto the pixels associated with the features of the guidewire, apredetermined length of the open curve is preserved.
 6. The method ofclaim 1, wherein updating the parameters defining the open curvecomprises using a gradient descent process that minimizes an energyfunctional defined on the open curve.
 7. The method of claim 6, furthercomprising using one of tangential forces or a pseudo-inverse to updatethe parameters defining the open curve.
 8. The method of claim 1,further comprising determining when convergence has occurred based on alocal minimum of an energy.
 9. The method of claim 8, furthercomprising, when it is determined that convergence has occurred, andwhen there is a second video image to process, determining across-correlation of feature maps to find displacement of the opencurve.
 10. The method of claim 9, further comprising setting an opencurve position in the second video image.
 11. A system for tracking aguidewire in video imagery, comprising: a memory device for storing aprogram; a processor in communication with the memory device, theprocessor operative with the program to: obtain a first video imageincluding pixels associated with features of a guidewire; select a setof parameters to define an open curve on the first video image;determine a feature map of the first video image using phase congruency;and update the parameters of the open curve using the feature map toalign the open curve to the pixels associated with the features of theguidewire.
 12. The system of claim 11, wherein when determining afeature map of the first video image using phase congruency, theprocessor is further operative with the program to perform multi-scaleimage analysis.
 13. The system of claim 11, wherein updating theparameters defining the open curve comprises energy minimization. 14.The method of claim 11, wherein updating the parameters defining theopen curve comprises using a gradient descent process that minimizes anenergy functional defined on the open curve.
 15. A method of tracking aguidewire in video imagery, comprising: obtaining a first video imageincluding features of a guidewire; selecting a set of control points fordefining a spline on the first video image; determining a feature map ofthe first video image using phase congruency; determining a gradient ofthe feature map; determining an arc length of the spline; determiningcurvature, blending coefficients, tangents and normals at a plurality ofpoints on the spline; determining a system of linear equations using thecurvature, blending coefficients, tangents and normals; inverting thesystem of linear equations to obtain the differential motion of thecontrol points; and evolving the control points.
 16. The method of claim15, wherein the gradient of the feature map is computed using centraldifferences.
 17. The method of claim 15, wherein the arc length of thespline is computed based on a derivative of the spline.
 18. The methodof claim 15, further comprising determining when convergence hasoccurred based on a local minimum of an energy.
 19. The method of claim18, wherein when it is determined that convergence has occurred, andwhen there is a second video image to process, computing across-correlation of feature maps to find displacement of the spline.20. The method of claim 19, further comprising setting a spline positionin the second video image using the displacement.